Cargando…

Machine learning-based chemical binding similarity using evolutionary relationships of target genes

Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To o...

Descripción completa

Detalles Bibliográficos
Autores principales: Park, Keunwan, Ko, Young-Joon, Durai, Prasannavenkatesh, Pan, Cheol-Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6846180/
https://www.ncbi.nlm.nih.gov/pubmed/31504818
http://dx.doi.org/10.1093/nar/gkz743
_version_ 1783468830462312448
author Park, Keunwan
Ko, Young-Joon
Durai, Prasannavenkatesh
Pan, Cheol-Ho
author_facet Park, Keunwan
Ko, Young-Joon
Durai, Prasannavenkatesh
Pan, Cheol-Ho
author_sort Park, Keunwan
collection PubMed
description Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information.
format Online
Article
Text
id pubmed-6846180
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-68461802019-11-18 Machine learning-based chemical binding similarity using evolutionary relationships of target genes Park, Keunwan Ko, Young-Joon Durai, Prasannavenkatesh Pan, Cheol-Ho Nucleic Acids Res Methods Online Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information. Oxford University Press 2019-11-18 2019-08-31 /pmc/articles/PMC6846180/ /pubmed/31504818 http://dx.doi.org/10.1093/nar/gkz743 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods Online
Park, Keunwan
Ko, Young-Joon
Durai, Prasannavenkatesh
Pan, Cheol-Ho
Machine learning-based chemical binding similarity using evolutionary relationships of target genes
title Machine learning-based chemical binding similarity using evolutionary relationships of target genes
title_full Machine learning-based chemical binding similarity using evolutionary relationships of target genes
title_fullStr Machine learning-based chemical binding similarity using evolutionary relationships of target genes
title_full_unstemmed Machine learning-based chemical binding similarity using evolutionary relationships of target genes
title_short Machine learning-based chemical binding similarity using evolutionary relationships of target genes
title_sort machine learning-based chemical binding similarity using evolutionary relationships of target genes
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6846180/
https://www.ncbi.nlm.nih.gov/pubmed/31504818
http://dx.doi.org/10.1093/nar/gkz743
work_keys_str_mv AT parkkeunwan machinelearningbasedchemicalbindingsimilarityusingevolutionaryrelationshipsoftargetgenes
AT koyoungjoon machinelearningbasedchemicalbindingsimilarityusingevolutionaryrelationshipsoftargetgenes
AT duraiprasannavenkatesh machinelearningbasedchemicalbindingsimilarityusingevolutionaryrelationshipsoftargetgenes
AT pancheolho machinelearningbasedchemicalbindingsimilarityusingevolutionaryrelationshipsoftargetgenes